The intricate web of interactions that define human behaviour, markets, and societies remains challenging to understand. The spread of information on social media and opinions among people are examples of how social interactions shape our world. Similarly, the interdependency of financial assets, like stocks and currencies, propagate shocks and regulate economic crises. The mathematical framework of time-evolving networks is used to study such complex systems holistically by modelling these interactions such as who is connected with whom and when precisely these connections happen. In this context, data are fundamental to understanding the world around us. In this project, we will develop mathematical methods and computational models to study social and economic systems using network science, agent-based modelling, and machine learning techniques. We will analyse the structure of social networks, how those networks emerge, and how they regulate diffusion processes such as opinion dynamics and shocks.